{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Classifier Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Lets create synthetic data points\n",
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=make_classification(n_samples=1000,n_features=2,n_classes=2,\n",
    "                        n_clusters_per_class=2,n_redundant=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21257104, -0.1960392 ],\n",
       "       [ 2.3821402 ,  0.27124703],\n",
       "       [ 1.03340474, -0.33726287],\n",
       "       ...,\n",
       "       [-0.45011412,  1.64659531],\n",
       "       [ 0.63552556, -0.73767318],\n",
       "       [-2.36576388,  2.23701447]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1,\n",
       "       1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,\n",
       "       1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
       "       0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 0, 1, 0, 1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.212571\n",
       "1      2.382140\n",
       "2      1.033405\n",
       "3      1.050006\n",
       "4      1.442964\n",
       "         ...   \n",
       "995    1.158510\n",
       "996    1.068731\n",
       "997   -0.450114\n",
       "998    0.635526\n",
       "999   -2.365764\n",
       "Name: 0, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0', ylabel='1'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(pd.DataFrame(X)[0],pd.DataFrame(X)[1],hue=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc=SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0521126 , 1.96313735]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Prediction\n",
    "y_pred=svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.84      0.86       132\n",
      "           1       0.83      0.86      0.85       118\n",
      "\n",
      "    accuracy                           0.85       250\n",
      "   macro avg       0.85      0.85      0.85       250\n",
      "weighted avg       0.85      0.85      0.85       250\n",
      "\n",
      "[[111  21]\n",
      " [ 16 102]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))\n",
    "print(confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf=SVC(kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Prediction\n",
    "y_pred1=rbf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.83      0.88       132\n",
      "           1       0.83      0.94      0.88       118\n",
      "\n",
      "    accuracy                           0.88       250\n",
      "   macro avg       0.89      0.89      0.88       250\n",
      "weighted avg       0.89      0.88      0.88       250\n",
      "\n",
      "[[110  22]\n",
      " [  7 111]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred1))\n",
    "print(confusion_matrix(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.87      0.84       132\n",
      "           1       0.84      0.77      0.81       118\n",
      "\n",
      "    accuracy                           0.82       250\n",
      "   macro avg       0.83      0.82      0.82       250\n",
      "weighted avg       0.83      0.82      0.82       250\n",
      "\n",
      "[[115  17]\n",
      " [ 27  91]]\n"
     ]
    }
   ],
   "source": [
    "polynomial=SVC(kernel='poly')\n",
    "polynomial.fit(X_train,y_train)\n",
    "## Prediction\n",
    "y_pred2=polynomial.predict(X_test)\n",
    "print(classification_report(y_test,y_pred2))\n",
    "print(confusion_matrix(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.73      0.79       132\n",
      "           1       0.74      0.85      0.79       118\n",
      "\n",
      "    accuracy                           0.79       250\n",
      "   macro avg       0.79      0.79      0.79       250\n",
      "weighted avg       0.79      0.79      0.79       250\n",
      "\n",
      "[[ 97  35]\n",
      " [ 18 100]]\n"
     ]
    }
   ],
   "source": [
    "sigmoid=SVC(kernel='sigmoid')\n",
    "sigmoid.fit(X_train,y_train)\n",
    "## Prediction\n",
    "y_pred3=sigmoid.predict(X_test)\n",
    "print(classification_report(y_test,y_pred3))\n",
    "print(confusion_matrix(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.3649506])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigmoid.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparametere Tuning With SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    " \n",
    "# defining parameter range\n",
    "param_grid = {'C': [0.1, 1, 10, 100, 1000],\n",
    "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "              'kernel': ['rbf']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid=GridSearchCV(SVC(),param_grid=param_grid,refit=True,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.940, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.907, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.867, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.847, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.847, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=0.1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.947, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.920, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.907, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.907, total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.907, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.940, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.867, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.847, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.847, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=1, gamma=0.0001, kernel=rbf ...................................\n",
      "[CV] ....... C=1, gamma=0.0001, kernel=rbf, score=0.507, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.947, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.947, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.907, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.940, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.947, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.940, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.867, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.847, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.847, total=   0.0s\n",
      "[CV] C=10, gamma=0.0001, kernel=rbf ..................................\n",
      "[CV] ...... C=10, gamma=0.0001, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.947, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.947, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.920, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.953, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.913, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.913, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.940, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.893, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.953, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=100, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=100, gamma=0.0001, kernel=rbf, score=0.940, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.940, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.953, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
      "[CV] ......... C=1000, gamma=1, kernel=rbf, score=0.960, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.947, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.907, total=   0.0s\n",
      "[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
      "[CV] ....... C=1000, gamma=0.1, kernel=rbf, score=0.940, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
      "[CV] ...... C=1000, gamma=0.01, kernel=rbf, score=0.933, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.873, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
      "[CV] ..... C=1000, gamma=0.001, kernel=rbf, score=0.947, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.927, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.880, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.887, total=   0.0s\n",
      "[CV] C=1000, gamma=0.0001, kernel=rbf ................................\n",
      "[CV] .... C=1000, gamma=0.0001, kernel=rbf, score=0.953, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         'kernel': ['rbf']},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1000, 'gamma': 1, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.91      0.93       132\n",
      "           1       0.90      0.95      0.93       118\n",
      "\n",
      "    accuracy                           0.93       250\n",
      "   macro avg       0.93      0.93      0.93       250\n",
      "weighted avg       0.93      0.93      0.93       250\n",
      "\n",
      "[[120  12]\n",
      " [  6 112]]\n"
     ]
    }
   ],
   "source": [
    "## Prediction\n",
    "y_pred4=grid.predict(X_test)\n",
    "print(classification_report(y_test,y_pred4))\n",
    "print(confusion_matrix(y_test,y_pred4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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